Toward reliable coherent cross-modal synthetic data: a novel framework for generative model construction and validation

Advancing precision oncology relies heavily on integrating diverse, multi-omics data; however, the widespread issue of incomplete patient registries severely limits the adoption of these models in standard clinical settings. To mitigate the impact of this missing information, this chapter presents Coherent Denoising. This scalable, ensemble-based generative diffusion methodology imputes missing modalities by conditioning on whatever clinical observations are present. The framework generates biologically plausible representations directly within highly compressed latent spaces. Empirical evaluations validate this framework using a large-scale cohort from The Cancer Genome Atlas (TCGA), comprising over 10,000 primary tumors across 20 cancer types. The dataset integrates four data modalities: copy number alterations, proteomics, transcriptomics, and histopathology whole-slide images. Results demonstrate that the synthesized profiles retain high topological fidelity and preserve the complex, non-linear signals required for downstream applications. Specifically, these synthetic representations successfully rescue predictive models from inference-time degradation when processing incomplete records, while counterfactual variance scoring provides an actionable metric to strategically prioritize diagnostic resource acquisition. Finally, the decoupled modular architecture provides robust, intrinsic privacy protections, structurally preventing unconditional database reconstruction and safeguarding sensitive patient distributions.